AI development that reaches production, not just a proof of concept.

That depends on choices we make together at the start, which is why we ask early. If your data can't leave Australia, we build on Australian regions or your own infrastructure and pick models accordingly. If it can't leave your network at all, hosted frontier models are out and we design around open weights instead. You get the answer in writing before the first prototype, it's far cheaper to design for than to retrofit.

We build the test set before we build the feature. On the voice agent we ship for a UAE client, that meant 94 real calls scored for intent, pricing, context and whether it ever invented an answer. It passed all 94 and hallucinated nothing. Without a test set you're shipping on impressions, and impressions don't survive a board asking how accurate it is.

Almost always the same thing. The pilot proved the model could do the task, and nobody had planned for permissions, audit trails, error handling, or who owns it once it's live. The demo was never the hard part. We scope those in the first week, which makes early progress look slower and is the reason the launch actually happens.

Yes, and that's most of our work. It goes inside your existing logins, permissions and audit trail, whether that's a custom platform, a SaaS product, or a WordPress or Shopify build. Very little of this is greenfield.

Yes. A cloud number is easy; answering the line already printed on your signage takes a SIP trunk into your carrier. We've done it with du and Etisalat in the UAE, and the same approach works with Australian providers. It's the difference between a demo people call to try and a system that picks up when a customer rings.

Yes. The voice agent we run handles Gulf Arabic, dialect and slang, not textbook phrasing, and holds context when someone asks two things at once. Language is configuration rather than a rebuild.

Less than most people expect, and rarely where they expect. We measure cost per resolved case, not per token, because a cheaper model that needs three attempts isn't cheaper. Routing the simple work to smaller models is usually the single biggest saving, and we build for that from the start.

Most businesses need neither. Good retrieval usually beats fine-tuning, costs less, and is far easier to change when your business changes. We'll say so plainly rather than sell you the more expensive answer, and where fine-tuning is genuinely right, we'll show you why.

Usually two to four weeks. We'd rather begin with a short paid discovery than a proposal written on assumptions. You get the scope, the risks and a written recommendation, and it's yours whether or not you carry on with us.

You do. The code, the prompts, the evaluation sets, the infrastructure. Your repositories, your cloud accounts, your provider keys. There's no layer of ours in the middle that you'd have to buy your way out of.